Reward Weighted Classifier-Free Guidance as Policy Improvement in Autoregressive Models
Consider an auto-regressive model that produces outputs x (e.g., answers to questions, molecules) each of which can be summarized by an attribute vector y (e.g., helpfulness vs. harmlessness, or bio-availability vs. lipophilicity). An arbitrary reward function r(y) encodes tradeoffs between these properties. Typically, tilting the model's sampling distribution to increase this reward is done at training time via reinforcement learning. However, if the reward function changes, re-alignment requires re-training. In this paper, we show that a reward weighted classifier-free guidance (RCFG) can act as a policy improvement operator in this setting, approximating tilting the sampling distributi...
| Authors | Alexander Peysakhovich, William Berman |
|---|---|
| Primary category | cs.LG |
| Published | 16 April 2026 |
| Last updated at source | 16 April 2026 |
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